Covariance

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Covariance

IV. Covariance V. Conditional Expectation VI. Normal Distribution


Lecture number:
3
Pages:
2
Type:
Lecture Note
School:
Cornell University
Course:
Econ 3120 - Applied Econometrics
Edition:
1

Unformatted text preview:

Lecture 3 Outline of Last Lecture I. Marginal Distributions II. Expectations and Variance III. Covariance Outline of Current Lecture IV. Covariance V. Conditional Expectation VI. Normal Distribution Current Lecture 2.3 Covariance When we are dealing with more than one random variable, it is useful to summarize how these two random variables move together. Suppose we have two random variables X and Y, and we define E(X) = µx and E(Y) = µy. The covariance between these two random variables is defined as Cov(X,Y) = σxy = E[(X − µx)(Y − µy)] We can also write the covariance as Cov(X,Y) = E[(X − µx)Y] = E[X(Y − µy)] = E(XY)− µxµy 2.3.1 Properties of the covariance: 1. If X and Y are independent, then Cov(X,Y) = 0 This follows since E(XY) = E(X)E(Y) when X and Y are independent. 2. For any constants a1,b1,a2,b2, Cov(a1X +b1,a2Y +b2) = a1a2Cov(X,Y) Now that we know about the Covariance, we can define a third property of the variance: 3. Var(aX +bY) = a 2Var(X) +b 2Var(Y) +2abCov(X,Y) One issue with covariance is that the units are difficult to interpret. It turns out that we can scale covariance by the standard deviations of both variables and to get the unit-friendly correlation: Corr(X,Y) = ρxy = Cov(X,Y) sd(X)sd(Y) = σxyσxσy 2.4 Conditional Expectation In econometrics we often want to know how much one variable X tells us about another variable Y. One way to do this is by using covariance and correlation, but another concept we will be using a lot is conditional expectation. Conditional expectation, written as E(Y|X = x) (often shortened to either E(Y|X) or E(Y|x)), tells us the mean of Y conditional on some value of X. The conditional expectation is defined in a similar way to the unconditional expectation above, but using the conditional probability distribution functions: discrete r.v.: E(Y|x) = ∑ y y · f(y|x) continuous r.v.: E(Y|x) = ˆ y y · f(y|x) Example: Suppose we are studying the relationship between schooling and earnings, and that hourly wages and schooling are our random variables. How does the mean wage vary with the schooling level? Our CEF (conditional expectation function might look something like this: E(WAGE|EDUC) = 4+0.6 ·EDUC Thus, for each level of schooling, we know the mean wage. Properties of the conditional expectation: 1. If X and Y are independent, then E(Y|X) = E(Y) 2. E(E(Y|X)) = E(Y) This is called the “law of iterated expectations.” Example: Suppose we want to know E(WAGE)in the example above, and we know that E(EDUC) = 11.5. Then, by the law of iterated expectations, E(WAGE) = E(E(WAGE|EDUC)) = E(4+0.6 ·EDUC) = 4+0.6 ·E(EDUC) = 4+0.6 · 11.5 = 10.9 3 Normal Distribution Because of its properties, the normal Econ 3120 1st Edition



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